Cloud data warehouses have undoubtedly made it easier for companies to collect and store their data. They’re flexible, faster, more scalable, and performant than any of the storage solutions that came before them. There’s just one problem with them – it’s very hard to get data out of them and into the apps a company uses every day.
We started Census in 2018 to fix this problem. In the two-and-a-half years we spent between then and now, building our simple tool into a more robust platform, the data landscape changed a lot. As more companies brought data closer to the heart of their operations, we saw the data warehouse become the center of that movement.
We launched publicly last year with the world's first reverse ETL that natively publishes from any data warehouse – the missing piece in the new, modern data stack. Using a reverse ETL, the teams inside companies that need customer data every day – sales, marketing, customer success – can access that information using their favorite tools.
What Is Reverse ETL?
Up until a couple years ago, data was largely being treated as it had since the 1970s – extract, transform, load (ETL). Data was copied from a system of record, then cleaned and transformed into a common storage structure, and loaded into the warehouse for storage.
Reverse ETL is the opposite of this, allowing companies to sync modeled customer data from the data warehouse to third-party apps like Salesforce, Hubspot, Marketo, Zendesk and many others. A reverse ETL comes with pre-built API integrations, taking the challenge of building and maintaining them out of the hands of data teams.
As I mentioned earlier, reverse ETL is the missing piece in a modern data stack, closing a customer data loop where information gets loaded from third-party apps into the warehouse, modeled inside, then synced back out to cloud apps.
A modern data stack generally consists of the following tools performing each of these four functions:
- Data Integration: an ETL tool that integrates every data source into the data warehouse. Recommended solutions are Fivetran or new comers like Airbyte
- Data Storage: a data warehouse that can store structured and unstructured data in one place. The heavy hitters are BigQuery, Snowflake, Redshift
- Data Modeling: a data modeling tool which helps manage data with a library of data models, making data usable for different purposes. You can look at tools like dbt.
- Data Activation: a data automation tool (👋 That's us!) will pull usable data out of your data warehouse, validate it automatically, and send it to the tools that need it.
Enabling Operational Analytics
Operational analytics informs day-to-day decisions with the goal of improving the efficiency and effectiveness of an organization’s operations. In simpler terms, it’s putting a company’s data to work so everyone can make smart decisions about the business.
When the data warehouse is connected to the rest of the business through a reverse ETL and a modern data stack, it enables operational analytics. Instead of using data to identify long-term trends and influence long-term strategy, operational analytics informs strategy for the day-to-day operations of the business. For example:
- CS and Ops teams can identify at-risk customers by surfacing customer usage data in a CRM or other tools. They can run reports or workflows to notify teams when usage ticks downward so that they can immediately take action to prevent churn.
- Driving new sales with product-qualified leads. Ops teams set up rules (combining activity and third-party signals) to discover potential sales accounts from aggregated freemium users and automatically sync these into a CRM as new leads. They also highlight the most active users in each lead or account so sales can prioritize who to call.
- Building personalized marketing campaigns. By merging product, support, and sales data, marketing teams build segments of users who should hear about new features and offers without bombarding the whole user base (e.g. notifying only users who experienced a pain point). As a bonus, they're able to send these automated emails "from" the internal account owner, which is synchronized via Census.
Operational analytics makes every decision made by each individual a strategic choice that’s backed by real-time data. Without a reverse ETL feeding complete customer data into apps, this couldn’t happen.
Supercharging Data Teams
Go-to-market teams aren’t the only ones who benefit when companies use a reverse ETL to enable operational analytics. Data teams, who have traditionally been charged with crunching reams of data in order to derive insights and build reports that answer questions about what happened in the business, can become active participants in creating strategy and success.
The traditional role of data or business intelligence teams has been, first and foremost, to report on how the business performed: revenue or usage KPIs from the last quarter, and so on. That type of reporting is only useful as quarterly check-in for leadership teams to adjust strategy. It doesn't drive any kind of automation or connect directly to the work of people on the front-lines of the business.
Instead of using analytics to look at what happened in the past, modern data teams become the central nervous system that drives products like an email marketing tool, a customer support tool, a sales tool, or even a finance tool. With high quality data available in these tools, data teams can build automated workflows that lead to actions – in other words, be data-driven.
If you’d like to learn more about what a reverse ETL can do for your company, schedule a demo and we’ll show you what we can do.